ISSN 1000-1239 CN 11-1777/TP

### Fine-Grained Interview Evaluation Method Based on Keyword Attention

Chen Chujie1, Lü Jianming1,2, Shen Huawei3

1. 1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006）

2 Key Laboratory of Big Data and Intelligent RobotSouth China University of Technology）, Ministry of Education, Guangzhou 510006）

3Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190）

• Online:2021-02-05
• Supported by:
This work was supported by the National Natural Science Foundation of China (61876065), the Natural Science Foundation of Guangdong Province (2018A0303130022), the Science and Technology Program of Guangzhou (201904010200) , and the Fundamental Research Funds for the Central Universities (D2182480, D2200150).

Abstract: Massive online interview video data provides an important data basis for intelligent interview evaluation. With the spread of the current global epidemic, the demand for online interviews has increased, as well as the intelligent interview evaluation tools. In a structured interview, the interviewer needs to observe the interviewee’s answers based on the evaluation criteria, and form a profile evaluation of the interviewee’s personality traits, communication skills, and leadership, so as to judge whether the interviewee’s characteristics match the position. Among them, personality evaluation is a widely accepted evaluation method among companies. Because personality traits affect people's language expression, interpersonal communication and other aspects, it is an important reference to assist the interviewer to decide whether an interviewee meets their job requirements. Based on this, a fine-grained interview evaluation method based on the LSTM(long short term memory) and the hierarchical keyword-question attention mechanism (HKQA-LSTM) is proposed, which aims to score the different personality dimensions of the interviewees and obtain a comprehensive interview score based on this. First, we effectively filter out important words and sentences that are closely related to personality traits in the interview dialogue by introducing a keyword attention mechanism. Then, we use keyword-question level attention mechanism and two-stage model learning mechanism on this basis. Fully combine the multi-scale contextual features of the texts expressed by interviewees to accurately predict personality traits. Finally, through the fusion of personality traits, a comprehensive interpretive evaluation result of the interview is obtained. The experimental results based on real interview scene data show that this method can effectively evaluate the interviewees' different personality traits scores and accurately predict the interviewees' overall scores.

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